Network data classification using graph partition

Application of network classification can be seen in many domains. These varies from preserving the quality of network to analyzing personal characteristics of network users. However current methods applied for network data classification does not meet the expectations. This is because networks are dynamic which are prone to rapid changes, while methods used for the classification has been either trained using examples or defined using heuristics. World Wide Web itself is a big graph which is made out of number of URLS connecting each other via hyper-links. Hence in this work we have used this graph nature of WWW and applied graph theories to partition the network to classify network data. We have used results obtained by classifying the network traffic using k-means algorithm to evaluate the performance and usability of proposed method.